In computed tomography, for improved material differentiation in a body to be imaged what is known as the multi-spectrum method (also referred to as the multiple energy method) or the two-spectra method (dual-energy method) is often applied. In such methods (approximately) the same point of the body is irradiated from (approximately) the same direction simultaneously or in turn by x-rays of different energies—in the multiple energy method this is generally a plurality of different x-rays (spectra), in the dual-energy method (a subordinate generic form of the multiple energy method) it is precisely two.
With multiple energy methods at least two different x-ray projections are thus created which result from the different typical energies. This enables the absorption characteristic of a body, specifically of an organic tissue or structures supported within said tissue, to be taken into account as well during the imaging: this absorption characteristic is namely decisively dependent on the energy of the x-ray radiation. Usually on the basis of the x-ray projection data from x-ray radiation with low typical energy a low-energy image and on the basis of x-ray projection data from x-ray radiation with high typical energy a high-energy image is reconstructed. These two images can then be combined with one another in order for example to create from them a soft tissue image or a bone image of a patient. With the aid of the multiple energy method a better discrimination of different materials within an area of the body to be imaged is possible in this way, such as the differentiation between bone tissue and contrast media in an examination area.
Thus a separate so-called single-energy image stack is created or computed from each acquisition with one energy in each case, which can be provided both singly (as already described) or in a combined image stack (with the respective other image stack(s)) for optimized output. In the latter case it is important for the output parameter of the combined image stack to be selected so that the user can be offered an optimized increase in their knowledge during viewing. An output optimization in this sense takes account for example of the so-called contrast-noise ratio (CNR), in which a maximum possible contrast in relation to a minimum possible noise is achieved in the combined image stack output.
Two basically different combination methods exist at present for combining a number of single-energy image stacks into a combined image stack, namely what is referred to as the optimum contrast method and another method in which a mono-energetic image stack is created.
The optimum contrast method is described for example in the article by Holmes, David, et al.: “Evaluation of non-linear blending in dual-energy computed tomography”. Eur J Radiol. 2008 December, 68(3), Pages 409 to 413, the entire contents of which are incorporated herein by reference. In this method an optimum ratio is computed with the aid of a non-linear algorithm from a low-energy and a high-energy portion of two dual-energy image stacks and the two image stacks are blended with one another, i.e. mixed. This involves a so-called sigmoidal blending, i.e. that the respectively determined optimum portions of the two image stacks result in the mixed image stack in a non-linear, namely sigmoidal manner.
The creation of a mono-energetic image stack is described for example in the article by Silva, Alvin et al.: “Dual-Energy (Spectral) CT: Applications in Abdominal Imaging”. RadioGraphics 2011, 31, pages 1031 to 1046, the entire contents of which are incorporated herein by reference. In this method, starting from the two (or more) image stacks presented created by measurement a further, virtual image stack is created which is based on an assumed (third) energy, which is usually different from the number of energies during image acquisition. The third energy assumed in this case is for its part selected so that an output optimization in the manner mentioned above will be obtained.
With the aid of the method presented here an output optimization can be obtained over an entire combined image stack, i.e. that the aim is always to optimize the output of the combined image stack as a function of a specific output interest. Specific image areas (such as specific organs or structures) are necessarily presented especially well while the imagability of other image areas also necessarily suffers from this approach.